重庆工商大学学报（自然科学版）2025，Vol.42Issue(4)：62-71,10.DOI:10.16055/j.issn.1672-058X.2025.0004.008
基于多特征信息融合的疲劳驾驶检测算法
Fatigue Driving Detection Algorithm Based on Multi-feature Information Fusion
摘要
Abstract
Objective Due to the current problem that driver fatigue detection cannot simultaneously balance detection speed and accuracy,this paper proposed a fatigue driving detection algorithm based on an improved YOLOv7 model.Methods Firstly,to improve the model's convergence speed and enhance its detection performance,the traditional convolutional layer was replaced with a depthwise over-parameterized convolutional layer,which accelerated the fitting process by adding learnable parameters.Secondly,in scenes with occluded targets,traditional downsampling processes can lead to significant feature loss.To improve the accuracy of occluded target detection,the algorithm introduced a Depthwise Separable Convolution(DS-Conv)module based on improved Squeeze-and-Excitation Attention.Thirdly,to enhance the model's ability to detect small targets,an MSS attention module with multi-scale feature extraction was added to the feature extraction layer,which can capture the details and contextual information of targets at different scales.Finally,fatigue driving determination was performed on the detected faces according to the PERCLOS criterion.Results The experimental results showed that the improved algorithm achieved accuracies of 96.0％,94.6％,and 88.1％on the Easy,Medium,and Hard subsets of the WIDER FACE dataset,respectively.Conclusion The improved algorithm,with its simple structure and small number of parameters,is conducive to real-time face target detection and is suitable for deployment in resource-limited environments such as in-vehicle systems,effectively ensuring driver safety.关键词
疲劳驾驶检测/多特征信息融合/通道注意力/多尺度特征提取/PERCLOS准则Key words
fatigue driving detection/multi-feature information fusion/channel attention/multi-scale feature extraction/PERCLOS criterion分类
交通工程引用本文复制引用
张兴旺,王凤随,杨海燕..基于多特征信息融合的疲劳驾驶检测算法[J].重庆工商大学学报（自然科学版）,2025,42(4):62-71,10.基金项目
安徽省自然科学基金(2108085MF197) （2108085MF197）
安徽高校省级自然科学研究重点项目(KJ2019A0162) （KJ2019A0162）
安徽工程大学国家自然科学基金预研项目(XJKY2022040). （XJKY2022040）
